Abstract:
Claim prediction plays a crucial role in the insurance industry, enabling companies
to design suitable insurance policies for potential policyholders. One approach to
predicting claims involves using the parameters of the gamma distribution. Several
methods can be applied, including Maximum Likelihood Estimation (MLE), the
Method of Moments (MoM), and the Bayesian method. This study focuses on
comparing the MoM and MLE methods to determine the most effective approach
for predicting insurance claim frequency using Google Collab. The analysis is
based on secondary data obtained from Kaggle. The MoM estimates parameters by
equating k sample moments with the corresponding k population moments, while
MLE works by maximizing the likelihood function. The findings indicate that MLE
produces a lower error rate of 0.424%, compared to 0.6845% for MoM. This
suggests that Maximum Likelihood Estimation (MLE) provides higher accuracy in
predicting insurance claim frequency.
